People analytics in the era of big data changing the way you attract, acquire, develop, and retain talent

1. 1Start with a business question, not a metric. The source defines effective People Analytics as analytics that start with a strategic human capital goal or question. Write one sentence naming the company objective the performance system must serve, for example 'Which behaviors and outputs actually move objective X?' Everything below traces back to it.
2. 2Link individual objectives to company objectives (the chapter's first section). Take each company objective and break it into the team and role-level outcomes that feed it. Write each employee's goals so that each one names the company objective it supports. A goal that cannot name its parent objective gets rewritten or dropped.
3. 3Define performance measures against business outcomes (the chapter's second section). For each role, pick measures that tie to the outcome in step 1. The Preface lists the real-time signals now available: engagement, actions, sentiment, productivity, quality, and aspirations. Use them only where they connect to the outcome. Record the data source and owner for each measure.
4. 4Integrate disparate data sources. Pull performance-review data, productivity and quality data, engagement data, and business-outcome data into one joinable view per employee. Without that join you cannot test whether a measure predicts the outcome.
5. 5Make feedback continuous rather than annual. Set a regular cadence at which each person and manager sees current numbers against goals, and use it to adjust goals and development actions. Log every adjustment so you can later see which interventions were followed by movement.
6. 6Create a prediction, then attach an action. The source requires analytics to 'create a prediction for the future' and 'lead to business actions with measurable results.' Test which measures predict outcomes or later success, and name the action each result triggers: coaching, stretch assignment, development plan, or promotion review.
7. 7Tie incentives and promotion to the validated measures (the chapter's fourth section). Promotion and incentive criteria use only measures from step 3 that survived step 6. Use the chapter's career-pathway approach (predictive analytics and graph theory) to map which roles lead to which, and where people with a given performance profile have moved.
8. 8Report to senior management (the chapter's fifth section). Give executives a short view of performance against company objectives, which measures predict results, and what actions were taken and what followed. This positions HR as a fact-based strategic partner, which is the Preface's stated aim.

The supplied passages contain only the chapter outline, not Chapter 9's case content, so this example is an illustrative construction, not a reported case. Suppose the company objective is faster customer-issue resolution. You write the business question: 'Which individual behaviors move resolution time?' Each support employee gets goals that name that objective as the parent. You define two measures, resolution quality and handling speed, and join them to engagement-survey data and to the company's resolution-time outcome. After a cycle, you test which measure tracks the outcome. Suppose quality does and speed alone does not. You then change the promotion criteria to weight quality, and you tell senior management what you tested, what you found, and what you changed. The test-then-act sequence is the point. The measures here are placeholders for your own.

• If a goal cannot be traced to a named company objective, rewrite or remove it before measuring it.
• If a measure is easy to collect but you cannot show it relates to a business outcome, keep it out of incentive and promotion decisions.
• If a prediction has no named action attached, do not report it. The source requires analytics to end in business actions with measurable results.
• If manager judgment and the data disagree on a promotion, investigate the gap with the data sources and measures in hand before deciding. Do not default to gut feel or 'that's worked in the past,' which the Preface rejects.
• If you cannot join performance data with business-outcome data, fix the data integration first. Do not add more measures.

• Measuring activity instead of outcomes. The tell: dashboards full of real-time signals (productivity, location, sentiment) with no line from any of them to a company objective.
• Cascading goals on paper only. The tell: employees cannot say which company objective their goals serve, or goals stay unchanged when company priorities shift.
• Falling back on gut feel for promotions. The tell: promotion outcomes cannot be explained by the stated performance measures.
• Reporting analysis without action. The tell: senior management receives findings but no record of what changed or what followed.
• Siloed data. The tell: performance, engagement, and business-outcome data sit in separate systems, so no one can test whether a measure predicts anything.

• ▸Company objective: name the specific objective this role supports.
• ▸Business question: one sentence on what you need to learn about performance for this objective.
• ▸Individual goals: list each goal and the company objective it names as its parent.
• ▸Performance measures: list each measure and why it connects to the outcome.
• ▸Data sources and owners: where each measure comes from, who owns it, and how it joins to the outcome data.
• ▸Prediction to test: which measure you expect to predict the outcome, and the evidence you will check.
• ▸Triggered action: what you do (coaching, development plan, promotion review) when the result comes in.
• ▸Incentive/promotion link: which validated measures feed pay or promotion decisions.
• ▸Senior management readout: what you will report, to whom, and when.

1. 1Start with a business question, not a model. The authors define effective People Analytics as analytics that 'start with a strategic human capital business goal or question.' Write one sentence such as 'Which high-value employees are likely to leave in the next period, and what does each departure cost us?' Do not pull data until that sentence exists.
2. 2Define retention before you measure it. Chapter 11 has a section titled 'What Is Retention, Anyway?' Fix the definition in writing: who counts as 'high-value', what counts as an exit (voluntary or involuntary), and the time window. Every later metric depends on these three choices.
3. 3Build the business case first, using the cost of employees as assets. Chapter 11 has a 'Business Case' section, and Chapter 10 treats employees as assets with a lifetime value and cost model. Tie each attrition event to the value the employee would have produced and the cost of replacing them. This ranks who is worth saving.
4. 4Integrate disparate data sources into one employee-level file. The preface requires integrating 'disparate data sources.' The passages name engagement surveys and engagement measures beyond the survey, onboarding and culture-fit outcomes, performance and promotion data, and lifetime value and cost data. Join them on a common employee key and keep a snapshot date for each field so you only use what was known before the exit.
5. 5Create a prediction for the future. Use the file to build the attrition model the authors call a 'proactive talent retention model.' Score each current employee on likelihood of leaving in the defined window. The output is a forward-looking risk score, not a retrospective exit report.
6. 6Explain the drivers, not just the score. The definition requires understanding 'key drivers of attrition.' List the variables that move risk most, such as engagement, onboarding experience, and performance or promotion path. Check each against the people closest to the work before it becomes a lever.
7. 7Segment the findings and match an intervention to each segment. Chapter 11 closes with 'The Segmentation Strategy of Talent Retention Model Insights.' Cross risk with employee value to get groups such as high value/high risk and low value/high risk. Assign each group a different action and spend the most on the first group.
8. 8Close the loop with measurable results. The authors require analytics that 'lead to business actions with measurable results.' Name an owner and a date for each intervention. Re-score after the window and compare the retention of treated employees against comparable untreated ones. Feed the result back into the model and the business case.

The source passages are front matter, a table of contents and the preface, and contain no retention case with figures. What they do give is the structure of Chapter 11 (written with Deloitte's Amel Arab and John Houston) and the preface's four-part test for effective analytics. The walk-through below is therefore illustrative and not a reported case. Take a company asking: 'Which high-value employees will we lose this year?' Step 1 is the goal and question. Step 2 is a written definition of retention covering who is 'high-value', which exits count, and the time window. Step 3 is the business case, pricing each departure against the employee's lifetime value and replacement cost. Step 4 is integrating disparate data: engagement survey results, onboarding outcomes, and performance and promotion history on one employee key. Step 5 is a risk score for each current employee. Step 6 is the drivers behind the score. Step 7 is segmentation: high-value/high-risk employees get individual retention actions, and lower-value/high-risk employees get lower-cost, lighter-touch actions. Step 8 is re-scoring after the window to check whether the interventions changed outcomes. The sequence matches the preface's test of goal, integrated data, prediction, and action with measurable results.

• If you cannot state the business question and the definition of 'retention' in two sentences, then stop and write them before touching data.
• If a predicted leaver is not a high-value employee under your lifetime value and cost model, then do not give them a costly intervention. Match the spend to the value.
• If a driver in the model cannot be changed by a manager or HR action, then use it only to target outreach, never as the lever.
• If an attrition finding does not map to a named segment with an owner and a date, then it is a report and not retention analytics. Assign the action or drop the finding.
• If you have only an annual engagement survey, then add behavioral and other engagement measures beyond the survey before relying on it to predict exits. The book treats moving beyond the survey as a separate step.

• Modeling with no business goal. The tell is a dashboard of attrition rates by department that no decision uses. The fix is to start from the question and the action it enables.
• Treating all leavers as equal. The tell is a single company-wide turnover number and one program for everyone. Value the employees, then segment, because the book frames retention around protecting the most valuable asset.
• Siloed data. The tell is a model built from HR records alone, with no engagement, onboarding or performance data. The preface requires integrating disparate sources.
• Stopping at the prediction. The tell is a ranked list of at-risk people that sits unused. Retention analytics ends with an action and a measured result, not a score.
• Relying on gut feel or 'that's worked in the past.' The tell is that interventions are chosen by anecdote and never compared with untreated peers. The authors say these approaches are no longer enough, and Chapter 11's heading says traditional approaches are failing.

• ▸Business question: one sentence naming the talent group and the outcome you want to protect.
• ▸Retention definition: who counts as high-value, which exits count, and the time window.
• ▸Value at stake: lifetime value and replacement cost per departure, with the source of each figure.
• ▸Data sources joined: list each source, such as engagement, onboarding, performance and promotion, and the employee key used to join them.
• ▸Prediction output: model score, window, and the date of the data snapshot.
• ▸Top drivers: the three to five variables that move risk most, and whether a manager or HR can act on each.
• ▸Segments and interventions: for each risk-by-value segment, the named action, the owner and the due date.
• ▸Success measure: the retention comparison you will run (treated vs. comparable untreated) and the date of the review.
• ▸Feedback decision: what you will change in the model or the interventions after the review.

1. 1Start from a talent question, not a program. The source frames every analysis around one of three questions: whom to hire, whom to engage, whom to retain. Write the wellness question in that form, for example 'Whom do we risk losing because of health, safety, or well-being problems?' Without it, you collect data with no decision attached.
2. 2Pair the sponsors. The SAS Employee Wellness and Health Program, the book's wellness case, was described by both its Chief Human Resources Officer (Jenn Mann) and its Chief Health Officer (Gale Adcock). Name an HR owner and a health or safety owner before you pull any data. One owns the business outcome and the other owns the program content.
3. 3Inventory data in the three source categories. Talent data: workforce well-being, overall wellness, turnover, engagement, performance, productivity, compensation and benefits. Company data: sales performance, revenue, churn, Net Promoter Score. Labor market data: Bureau of Labor Statistics turnover, job openings, wages, and unemployment, cut by industry, company size, occupation, state, and city. Mark which fields you hold, which you lack, and which sit in other systems.
4. 4Link each data stream to the question and drop what does not connect. The source says that identifying the streams and linking them to your business question shows which data points are key to an actionable plan. Keep only the fields that would change a decision. Wellness data with no link to retention, engagement, performance, or cost is reporting, not analytics.
5. 5Cover all three parts of the scope: wellness, health, and safety. Chapter 12 treats them together as one pillar, driving 'business performance and loyalty'. Pick at least one outcome measure for each part, and one business-side measure such as productivity, turnover, or customer loyalty.
6. 6Turn the question into a measurable prediction. The source says to use predictive models to anticipate what will happen with your employee base, and to tie the prediction to strategic actions and measurable goals. State the prediction, the metric, and the target date. Example form: 'Employees in segment X with wellness indicator Y are more likely to leave; the action is Z; success is a lower turnover rate in that segment.'
7. 7Benchmark externally. Use the labor-market data (turnover rates, wages, job openings) to separate a company-specific wellness problem from an industry-wide pattern before you spend on a program.
8. 8Segment the findings before acting. The retention chapter's segmentation strategy applies here too: group employees by risk and need, and assign a different action to each group. Review the measures on a fixed cadence and keep or cut programs by their measured effect.

The source's real example is the SAS Employee Wellness and Health Program. The authors interviewed its Chief Human Resources Officer, Jenn Mann, and Chief Health Officer, Gale Adcock, for the case. The passages give no results, so the outcomes are not reproduced here. What the example does show is the structure: an HR executive and a health executive jointly own the program. To apply the book's method, take the question 'Whom do we risk losing?' Pull talent data (wellness and well-being indicators, engagement, performance, turnover), company data (revenue and customer churn for the affected teams), and labor market data (BLS turnover and job openings for your occupation and city). Link them, and keep only the fields that bear on the retention question. Build a predictive model of who is likely to leave and what role wellness indicators play. Then set one measurable goal, such as lower turnover in the highest-risk segment, and assign an action to each segment. The numbers and the model results are yours to supply. The source gives the process, not the figures.

• If you cannot phrase the wellness effort as a hire, engage, or retain question, then stop and reframe it before collecting data.
• If a data field cannot change a decision or action, then leave it out of the model.
• If your turnover problem matches the BLS industry turnover rate for your occupation and location, then treat it as a market condition and not as proof that your wellness program failed or is needed.
• If the HR owner and the health or safety owner disagree on the outcome metric, then settle it before launch, since each reads success differently.
• If a prediction has no linked action and measurable goal, then it is not ready to present to leadership.

• Program counting instead of impact measurement: reports show enrollments and events, but no business outcome such as turnover, engagement, or productivity. The tell: every slide measures activity.
• Siloed ownership: wellness data stays with the health function and never joins HR or company data. The tell: no analysis connects wellness to performance, churn, or revenue.
• Data hoarding: pulling every available field without a business question. The tell: a long data inventory and no stated decision.
• Averages hiding risk: reporting one company-wide wellness figure and not segmenting. The tell: a flat headline number while specific groups keep leaving.
• Ignoring the labor market: crediting or blaming the program for turnover changes that match industry trends. The tell: no external benchmark appears in the analysis.

• ▸Business question: write it as a hire, engage, or retain question.
• ▸HR owner and health/safety owner: name both people.
• ▸Scope covered: tick wellness, health, and safety, and name one outcome measure for each.
• ▸Talent data available: list wellness or well-being, engagement, turnover, performance, productivity, and benefits fields, and where each is held.
• ▸Company data linked: list revenue, churn, NPS, or sales measures tied to the affected groups.
• ▸Labor market benchmark: give the BLS turnover or job-openings figure for your industry, occupation, and location.
• ▸Prediction statement: state who is expected to do what, and the model used.
• ▸Segment-to-action map: for each risk group, name the action and the person responsible.
• ▸Measurable goal and review date: give the metric, the target, and the date of the next review.

1. 1Start from strategy, not from requisitions. Per the foreword, a goal such as 'increase market share by x percent' is a wish, not a purpose. Write down the organization's purpose, goals, and strategic plan first, then list the roles and skills those goals require. Demand that is not tied to strategy cannot be tested for fit.
2. 2Define demand by skill and role, not by headcount alone. For each critical role, record the number needed, the skill set, the experience level, and the location. These are the same cuts the Bloomberg location-intelligence model uses: skill set, function, experience level.
3. 3Quantify external supply for each hard-to-fill role. Answer the passages' questions: How popular is the skill set? How popular within the function and at that experience level? How many companies are hiring for similar roles? Which nearby schools teach relevant courses?
4. 4Compare supply and demand by location and price point. Ask: How many people can we hire? What skills are reasonable at this price point? Which city has the best talent supply? Rank locations on those answers rather than defaulting to the existing office.
5. 5Forecast scarcity and test the hiring target. Use a predictive model to forecast talent scarcity for the role in each location, then ask whether the planned number is right given the supply/demand balance. For scarce roles the answer can be 'hire more than planned.'
6. 6Reset hiring goals with the business. Bring the forecast to leadership as a data-driven recommendation. Agree on realistic hiring numbers, baseline timelines, and budget, and adjust sourcing tactics by source-of-hire performance (career site, job boards, social media, referrals, campuses).
7. 7Stress-test over a multi-year horizon. Build or request a forward view (Bloomberg uses five years) that pressure-tests human capital programs and sets reasonable hiring targets. Account for known pressures such as the aging workforce, retirements, and STEM shortages.
8. 8Close the loop on the supply side. Feed retention data, such as flight-risk models, into the supply estimate. Internal attrition shrinks supply as surely as a weak hiring market does, so refresh the balance on a regular cadence.

Bloomberg's talent acquisition team applies this directly. Using location and intelligence models, it identifies when and where to source and what the competitive landscape looks like in a given market. For each role it asks how popular the skill set is, how many other companies want similar roles, and which nearby schools teach relevant courses. Suppose the business plans to hire five data scientists. Instead of accepting five, the team uses predictive insights to forecast data-scientist scarcity by location and to test whether five is the right number given the supply/demand balance. For hard-to-fill roles such as this, the model can recommend hiring more people than initially planned. The team also reports which city has the best supply and what skills are reasonable at a given price point. The conversation with managers shifts from 'fill this req' to 'here is what the market allows,' which yields more realistic hiring goals and a faster hiring process. Bloomberg's in-house models also look five years ahead to stress-test human capital programs and set reasonable targets.

• If a demand request cannot be traced to a stated strategic goal, then do not source against it until the business states the purpose it serves.
• If forecast scarcity for a skill in a location is high, then raise the hiring number, widen the location search, or raise the price point rather than holding the original target.
• If a market has many companies competing for the same skill set and few nearby schools producing it, then treat it as a 'dogfight' market and shift sourcing to a location with better supply.
• If a source of hire underperforms on volume or post-hire performance, then reallocate hiring budget to the sources that deliver for that specific position type.
• If retention models flag rising flight risk in a scarce-skill group, then count the expected loss against supply before approving the plan.

• Planning on headcount alone and ignoring skills. Tell: requisitions are filled on time but managers still report capability gaps.
• Relying on common recruiting tools (job boards, campus trips, ads) that every competitor uses. Tell: your results look like everyone else's and don't reflect future talent needs.
• Accepting the requested hiring number without a supply check. Tell: roles stay open for months and targets are missed repeatedly.
• Treating strategy statements as plans. Tell: demand is described as 'grow x percent' with no role- or skill-level translation.
• Looking only at today's supply and ignoring the future horizon, such as retirements and STEM shortages. Tell: a surprise shortage appears after a large share of a skill group leaves or the market tightens.

• ▸Strategic goal served: the stated business purpose or goal this role supports.
• ▸Role and skill set: the title and specific skills required.
• ▸Experience level and function: the seniority and function used to size the market.
• ▸Planned headcount: the number the business is requesting.
• ▸Market demand signal: how many companies are hiring similar roles and how popular the skill set is.
• ▸Supply signal by location: nearby schools with relevant courses and the talent available in each candidate city.
• ▸Price point: the compensation at which the needed skills are reasonable to hire.
• ▸Forecast scarcity and recommended headcount: the predicted scarcity and whether to hold, raise, or lower the number.
• ▸Internal attrition risk: flight-risk exposure in this skill group that reduces supply.

1. 1Split the definition into four measurable components: job performance, productivity, cultural fit, and tenure. The passages give no single formula, so write down what each component means for the role and name the data source for each before you score anything.
2. 2Anchor performance to business outcomes, not manager impressions. The source argues for tying talent to business outcomes and moving past 'gut feel' and 'that's worked in the past'. Define the performance measure for the role and link it to a company objective (the Chapter 9 approach of linking individual objectives to company objectives).
3. 3Measure time to productivity for every hire. Chapter 7 lists it as an outcome measure. Record the tenure point at which the hire's daily performance value first exceeds daily cost (the daily breakeven).
4. 4Track tenure against cumulative breakeven. Sum daily surpluses and losses, including recruiting and training debt, and note when the cumulative line crosses zero. A hire who leaves before that point is a net cost, however well they performed.
5. 5Capture cultural fit and early-stage signals from onboarding. Chapter 7 treats culture fit and onboarding as measurable and supports an onboarding predictive model. Record onboarding outcomes alongside performance so fit is evidence, not impression.
6. 6Tag every hire with its source and the selection data used: sourcing channel, application, preinterview assessment, interview result. Chapters 5 and 6 treat sourcing and acquisition as analytic stages. Without these tags you cannot trace quality back to a process.
7. 7Compare cohorts, not individuals. Group hires by source channel or assessment profile and compare time to productivity, performance, and early attrition. Quality of hire is a verdict on sourcing and acquisition, so the unit of analysis is the process.
8. 8Feed the findings into a predictive model for selection (Chapter 6: 'Putting It All Together: Predictive Analytics for Selection'). Use the pre-hire data that separated strong cohorts from weak ones, then recheck the model against the next cohort.

The source's stylized employee-value model shows why tenure belongs inside quality of hire. In the example, the employee costs the business money from day one. At nine months daily performance crosses daily cost, the 'daily breakeven', and the employee delivers net positive contribution from then on. Recruiting and training debt is not repaid until 24 months, the cumulative breakeven. An employee who leaves at nine months represents a net cost of more than $25,000 on average in lost start-up costs. The source also notes that it often sees impressive attrition after just three to six months. Apply this to two hires from different sourcing channels. If hire A leaves at five months and hire B stays past 24 months, a performance rating taken at month four cannot separate them. The cumulative value curve does: A never reaches either breakeven, and B passes both. Compare cohorts by channel and you can see which source delivers hires who cross the breakeven lines. That is the quality-of-hire reading of the sourcing process. These figures are the source's illustration, so set your own breakeven points from your own cost and performance data.

• If a hire leaves before the cumulative breakeven, score the hire as a net cost whatever the performance rating, because recruiting and training debt was never repaid.
• If early attrition clusters at three to six months, investigate sourcing, selection, and onboarding for that cohort before blaming the individuals.
• If a channel or assessment profile produces hires with a shorter time to productivity and longer tenure, treat that as predictive evidence and test it on the next cohort before changing selection.
• If a judgment of fit rests only on interviewer feel, pair it with an onboarding or performance outcome measure before using it to score quality.
• If you are comparing long-tenure hires, adjust values for inflation, as the source advises for very long tenures.

• Scoring quality of hire from manager impressions alone. The tell: the score cannot be linked to any business outcome or objective.
• Ignoring tenure when judging performance. The tell: a 'top performer' left within months and the sourcing channel still gets credit.
• Judging individuals, not cohorts. The tell: you cannot say which source or assessment produces better hires.
• Assuming short tenure is just a generational problem. The tell: you discount early exits as 'millennials switching jobs', although the Federal Reserve Bank of Atlanta data cited in the source show median tenure declining across age groups. Median job tenure for 20- to 30-year-olds fell from four years (born 1953) to one year (born 1993), so tenure targets must reflect that.
• Measuring after the fact with no pre-hire data. The tell: you can describe good hires but cannot name which source or selection signal predicted them.

• ▸Role and hire cohort: name the role and the hiring period being evaluated.
• ▸Source channel: record where each hire in the cohort came from.
• ▸Selection data used: list the assessments and interview results captured before the offer.
• ▸Performance measure and result: state the business-linked measure and each hire's value against it.
• ▸Time to productivity: record the tenure point at which daily value first exceeds daily cost.
• ▸Onboarding and culture-fit outcome: record the onboarding result you chose as the fit evidence.
• ▸Tenure status: record who has stayed or left, and when, especially exits in months three to six.
• ▸Cumulative breakeven status: record whether startup debt (recruiting and training) has been repaid, using your own cost data.
• ▸Verdict and next action: say whether the source or selection signal is working, and which change you will test on the next cohort.

1. 1Start from purpose, not from a values poster. The foreword's point is that a market-share target is 'nothing more than a wish'; purpose must be stated as the organization's role in society. Write that purpose in one or two sentences first, because culture fit means fit with something specific.
2. 2Frame the business question before pulling any data. The source says People Analytics must be 'grounded in key business questions' and warns of 'analysis paralysis'. Write one question, such as 'Which new hires fail to integrate socially, and what do they have in common?', and rank it against your other questions.
3. 3Define the dominant culture in observable terms: norms, behaviors, and values. Only 12 percent of HR and business leaders report having a program to define and build a strong culture, so expect to build this definition yourself. Use employee research and behavioral data, not leadership opinion alone.
4. 4Define the individual side the same way. Capture the new hire's values, beliefs, and behaviors from the preinterview assessments and interviews (Chapter 6 covers assessment analytics and predictive selection). Use matching categories so the two sides can be compared.
5. 5Instrument onboarding as the integration window. The book's Chapter 7 splits it into stages and early employee needs, and its OPEN framework structures the analysis. Collect data at each stage: engagement pulse, manager check-ins, early behavior. This is where social integration first shows up in data.
6. 6Pick outcome measures before you model. Chapter 7 names 'time to productivity and other outcome measures'. Add retention, since the book treats attrition as a downstream result of poor fit, and quality of hire (the source credits Rob Macintosh for insight on it).
7. 7Move from descriptive to predictive. Descriptive: what has happened (who left, who ramped slowly). Predictive: which fit signals forecast outcomes. Prescriptive: what to do for a given hire. Build the onboarding predictive model (Chapter 7) on fit measures plus outcomes, then attach an action to each risk pattern.
8. 8Integrate sources across the employee lifecycle. The source says to include candidates, current employees, and former employees (alumni). Exit data from alumni is a check on whether your fit definition is real.

The passages give no company case on culture fit alone, so this example applies their statistics and framework to a hypothetical scenario. Deloitte's global survey of 3,300+ leaders in 106 countries found only 12 percent had a program to define and build a strong culture, and only 7 percent rated themselves excellent at measuring engagement and retention. Suppose you are in the other 88 percent. Step one is to write the purpose and the observable cultural norms. Step two is to add fit and onboarding-stage measures to the hire records you already have. The descriptive view then shows who left or ramped slowly. The predictive view tests which fit signals forecast time to productivity and early attrition. The prescriptive view assigns an intervention to each pattern, which is the doctor-and-pharmacy analogy in the foreword: the diagnosis is descriptive, the treatment choice is predictive, and the written instructions are prescriptive. The deliverable is a model that tells an onboarding manager which new hires need extra integration support in the first stage.

• If you cannot state the organization's purpose and norms in observable behaviors, then do not build a fit model yet. Define the culture first, because the source says purpose is seldom fully addressed.
• If the analysis does not answer a ranked business question, then drop it. The source warns that data volume pulls teams into analysis paralysis and intellectual curiosity.
• If you only have descriptive reporting on fit (turnover rates, survey scores), then treat it as a baseline and build toward predictive. The source holds that People Analytics has the most impact when it is forward-looking.
• If fit scores predict hiring success but not time to productivity or retention, then distrust the fit definition and revisit it. Outcome measures are the test of the construct.
• If a hire shows weak fit signals during onboarding, then respond with a prescriptive action in the early stage tied to early employee needs, not an exit-stage fix.

• Treating fit as a gut-feel interview impression. The tell: no written culture definition and no outcome data to check the 'fit' call against.
• Equating a strategic target with culture or purpose. The tell: the 'values' reduce to goals like increasing market share by x percent, which the foreword calls wishes.
• Measuring fit once at hire and never again. The tell: no data from onboarding stages, so you cannot see when integration breaks down.
• Running only descriptive HR reporting in an isolated dashboard. The tell: reports explain past turnover but never change a hiring or onboarding decision, which is the 'isolated reporting and dashboard mentality' the source says People Analytics is moving away from.
• Ignoring alumni and candidates as data sources. The tell: your fit definition comes only from survivors, so it describes who stayed rather than who fits.

• ▸Organizational purpose: state the role in society in one or two sentences, not a numeric target.
• ▸Business question: the single ranked question this fit analysis answers.
• ▸Observable cultural norms: three to five behaviors and values that describe the dominant culture.
• ▸Individual fit signals: the assessment, interview, and early-behavior data that capture each hire's values and behaviors.
• ▸Onboarding stage checkpoints: each stage of onboarding and the measure you collect at it.
• ▸Outcome measures: time to productivity, quality of hire, retention, engagement.
• ▸Data sources (lifecycle): candidates, current employees, alumni; note what each contributes.
• ▸Descriptive baseline: what has already happened (attrition, slow ramp) and for whom.
• ▸Predictive and prescriptive link: the fit pattern you test as a predictor and the action it triggers.

1. 1Start from a business question, not a survey. The source frames it as: 'How engaged are our top performers in our key business strategies?' followed by 'Do we know which talents are at risk of leaving? What are we doing to keep them?' Write the question you must answer and the decision it will change before you collect anything.
2. 2Split the definition into measurable parts: commitment to organizational goals, motivation to contribute, well-being and enthusiasm, and discretionary effort. Assign each part at least one indicator, so 'engagement' is not one blended score.
3. 3Run the engagement survey as a baseline, not as the end product. The book treats the survey as the first of four steps: importance, surveys, making surveys predictive, and moving beyond the survey. Record the survey date, population, and response coverage so later analysis can join it to other data.
4. 4Make the survey predictive. Join each respondent's survey results to later outcomes you already hold: attrition, performance, time to productivity. Then test which items or patterns precede those outcomes. A score that predicts nothing is a descriptive statistic.
5. 5Add signals beyond the survey. The foreword lists the real-time data now available: engagement, actions, sentiment, productivity, location, quality, and aspirations. Choose a few that map to your component indicators from step 2 and track them continuously between survey waves.
6. 6Tie engagement to objectives and outcomes. Link individual objectives to company objectives (the performance management chapter's logic) so you can see whether engagement is high on the work the business depends on, not just high overall.
7. 7Segment before you act. The retention chapter closes with a 'segmentation strategy' for model insights. Cut engagement by role, performance tier, and tenure, with top performers and early-tenure employees as priority segments, then assign a specific response to each at-risk segment.
8. 8Check the front of the lifecycle. The book groups onboarding, culture fit, and engagement into one pillar and measures time to productivity as an onboarding outcome. Review new-hire engagement separately from tenured-employee engagement. Set privacy and data-use limits in advance, because the book flags an 'employee data privacy backlash' as a coming risk.

The passages give the structure of the engagement analysis but no company case with numbers, so this walk-through is illustrative and built only from the source's own questions. A human capital leader asks the foreword's question: how engaged are our top performers in our key business strategies? First, she defines the top-performer segment from performance data. Second, she joins the latest engagement survey results to that segment. Third, she checks which survey patterns preceded departures in earlier periods, which is the 'make the survey predictive' step. Fourth, she adds between-survey signals such as sentiment and productivity to see whether the survey is already stale. Fifth, she asks the foreword's follow-ups: which of these talents are at risk of leaving, and what are we doing to keep them? Her output is a segmented list of at-risk top performers, each with a named response and an owner. Her next survey wave then tests whether that segment's indicators moved.

• If you cannot name the decision an engagement result will change, then do not field the survey yet; the source's engagement work starts from strategic questions about top performers and retention.
• If survey scores have not been tested against later outcomes such as attrition or productivity, then treat them as descriptive only and do not use them to forecast.
• If the overall average looks healthy, then cut it by segment (top performers, tenure, role) before concluding anything; the book's retention work depends on segmentation.
• If survey waves are far apart and the business is changing quickly, then add real-time signals (sentiment, actions, productivity) to cover the gap.
• If a new data source touches employee location, behavior, or sentiment, then settle disclosure and permitted use first; the book warns of a privacy backlash.

• Reporting one engagement number. The tell: leaders quote the score but cannot say whether commitment, motivation, well-being, or discretionary effort is driving it.
• Treating the survey as the finish line. The tell: results are presented, an action plan is written, and no later outcome such as attrition or performance is ever linked back.
• Looking only at averages. The tell: the company-wide score is stable while top performers or new hires leave; the book's closing chapter says analytics must move 'beyond the averages'.
• Running on gut feel. The tell: the justification is 'that's worked in the past', which the preface calls no longer enough, rather than data tied to a business outcome.
• Collecting behavioral or sentiment data without employee trust. The tell: participation or candor falls, or employees question how the data is used.

• ▸Business question: the strategic question this analysis answers (e.g., how engaged are our top performers in key strategies?).
• ▸Decision to change: the specific action that will differ depending on the result.
• ▸Component indicators: one or more measures each for commitment, motivation, well-being/enthusiasm, and discretionary effort.
• ▸Priority segments: top performers, new hires, critical roles, or other groups to analyze separately.
• ▸Survey baseline: wave date, population, and response coverage.
• ▸Outcome link: the later outcome (attrition, performance, time to productivity) the survey will be tested against.
• ▸Between-wave signals: which real-time data (sentiment, actions, productivity, quality) you will track.
• ▸Segment response plan: for each at-risk segment, the action and its owner.
• ▸Privacy and use limits: what data is collected, who sees it, and what it will not be used for.

1. 1Write the construct as outcomes first. The source defines performance against predefined goals and business outcomes, so list the 3-5 business outcomes the role exists to move before choosing any metric. Chapter 9 of the source opens with this step: link individual objectives to company objectives.
2. 2Cascade each company objective down to an individual objective. For every person or role, you should be able to trace one line from their objective to a company objective. If a role has an objective with no line upward, drop it or rewrite it.
3. 3Define performance measures for each objective, covering both efficiency (output) and effectiveness (quality). The Piper case shows why: management saw an output-and-quality problem, so a single output metric would have hidden the cause. Pair every volume measure with a quality measure.
4. 4Consolidate the data. The source warns that employee data sits in finance, HR systems, and IT. Pull productivity, quality, engagement, and tenure data into one table keyed by employee and period before you analyze anything.
5. 5Add a time-to-productivity measure for new hires. The onboarding chapter lists 'Time to Productivity' as an outcome measure, so record how long each hire takes to reach the productivity standard for the role.
6. 6Test the stated cause of any productivity gap before acting. Compare the metric against turnover, tenure, ongoing training, and development, not only against equipment and process. Piper's leadership assumed equipment, layout, and quality control. The cause turned out to be employment levels and high staff turnover.
7. 7Link incentives and promotion to the defined measures, then report results to senior management as findings tied to business outcomes. This replaces decisions based on 'gut feel' and 'that's worked in the past', which the source says are no longer enough.
8. 8Move from description to prediction. Once the measures are stable, model which employees, segments, or conditions precede high or low performance, and use graph and predictive methods for career pathways and promotion.

Piper, a UK window and door manufacturer, ran one site with a commercial unit and a domestic unit, and in 2000 it wanted more output without losing quality. Management believed frontline productivity and quality control were the problem, and blamed poor equipment use and bad layout. The UK Department of Trade analyzed the operation. It confirmed that productivity and quality were serious threats, but traced the basic cause to poor levels of employment and high staff turnover. The company had won regional awards for training, but that training covered only new hires, with no ongoing training or staff development. Leadership stayed convinced that better use of current operations was the answer and never accepted turnover as a threat. Piper filed for bankruptcy in late 2013. The lesson for your own productivity measurement is to put turnover and development data next to every output and quality metric before you accept a cause.

• If a productivity or quality metric drops, then check turnover, tenure mix, and ongoing development data before blaming equipment or process.
• If an individual objective cannot be traced to a company objective, then rewrite or remove it before measuring it.
• If a measure captures only volume, then add a quality measure so you do not trade one for the other.
• If the employee data sits in separate finance, HR, and IT systems, then merge it into a single employee-level dataset before modeling. Do not wait for one exceptional data scientist to fix it.
• If leadership resists a finding that contradicts its assumption, then present it as a link to a business outcome. Piper shows the cost of leaving such an assumption unchallenged.

• Fixing the visible symptom. The tell is that the remediation plan covers only equipment, layout, and process, with no people data in it. This is Piper's mistake.
• Training only at the start. The tell is strong onboarding and training awards but no ongoing training or staff development, with output still stalling.
• Measuring against goals nobody tied to the business. The tell is individual targets that cannot be traced to any company objective.
• Relying on gut feel and precedent. The tell is that decisions are justified with 'that's worked in the past' rather than data tied to outcomes.
• Betting on one data genius. The tell is a single analyst holding the data and a dataset still split across finance, HR, and IT, with no shared framework or process.

• ▸Role / employee group: name the role you are measuring.
• ▸Company objective served: the business outcome this role moves.
• ▸Individual objective: one line that traces up to the company objective.
• ▸Output (efficiency) measure: the volume or speed metric and where the data lives.
• ▸Quality (effectiveness) measure: the paired metric that guards against trading quality for volume.
• ▸Time to productivity: how long a new hire takes to reach the standard for this role.
• ▸People-side checks: turnover, tenure, and ongoing training or development data to compare against the metric.
• ▸Incentive or promotion link: how results feed rewards or advancement.
• ▸Senior management insight: the finding, stated as its effect on a business outcome, plus the decision it should drive.

1. 1Scope the construct as one system before you measure anything. The book groups wellness, health, and safety into a single pillar (Chapter 12 and the 'Employee Wellness, Health, and Safety Pillar'), so write one definition that covers physical, mental, and emotional health plus the safety of the work environment. Do not let a gym-membership program stand in for the whole.
2. 2State the business question in the chapter's own terms: wellness drives business performance and loyalty. Write one sentence linking a well-being measure to one outcome you already track, such as retention, engagement, or safety incidents. If you cannot name the outcome, you are not ready to build the analysis.
3. 3Name two joint owners, one from HR and one from health. The SAS Institute program in the source was described by both its Vice President of HR (Jenn Mann) and its Chief Health Officer (Gale Adcock). Give the HR owner program and policy; give the health owner clinical credibility and data judgment.
4. 4Inventory the data you hold on each part of the construct. List what exists on program participation, health-risk indicators, safety incidents, and workplace-safety claims or insurance records, and who holds each. Note gaps explicitly. The source's Deloitte survey found 60% of HR and business leaders lacked an adequate program to measure and improve engagement, and measurement gaps like this are the norm.
5. 5Build a descriptive baseline first. Report current levels and participation by group, location, and role. The chapter sequence puts program best practices ahead of 'Optimizing Your Employee Wellness Health and Workplace Safety with Predictive Analytics'. Prediction on a thin baseline produces noise.
6. 6Segment before you act. Use the segmentation logic the book applies to retention (Chapter 11): split the population and design different interventions for different groups, so you do not run one program for everyone.
7. 7Join well-being data to retention and engagement data, then test whether lower-well-being or higher-risk groups leave, disengage, or have more safety events. Only after that relationship holds, move to a predictive model that flags groups at risk early.
8. 8Set privacy rules before collecting health data. The book's final chapter flags an 'Employee Data Privacy Backlash'. Decide who can see individual-level data, report only aggregates to managers, and tell employees what is collected and why.

The passages give no figures for the SAS Employee Wellness and Health Program, only that its Vice President of HR and its Chief Health Officer were interviewed about its implementation. The example below is therefore built from what the source does establish, and it contains no invented results. The Deloitte 2015 survey of 3,300+ HR and business leaders in 106 countries found engagement rated 'very important' by 50% of leaders, up from 26%, yet 60% had no adequate program to measure and improve it and only 7% rated themselves excellent at measuring and improving engagement and retention. Take an organization in that position. Step one: appoint an HR lead and a health lead, as SAS did. Step two: list existing wellness, safety, and engagement data and mark what is missing. Step three: report a baseline by segment. Step four: compare well-being and safety indicators against attrition and engagement for each segment. Step five: only when the link holds, build a predictive model of risk. This order puts the measurement gap Deloitte found ahead of the model, which is where the Chapter 12 sequence places it.

• If you cannot report a baseline by segment, then do not build a predictive model; fix measurement first.
• If a well-being measure has no link to a retention, engagement, or safety outcome you already track, then treat it as a program-activity metric, not as evidence of impact.
• If a segment shows low participation or high risk, then design a targeted intervention for it instead of expanding the all-employee program.
• If the analysis requires individual-level health data, then limit access to the health owner and report only aggregates to managers and HR.
• If HR and health leaders disagree on what counts as success, then resolve it before launch by agreeing on one outcome and one measure.

• Equating wellness with a perk menu. The tell: the only metric is enrollment or participation, and safety and mental health are absent from the report.
• Skipping the baseline and going straight to prediction because analytics is fashionable. The tell: model outputs cannot be compared to any prior-period level.
• Running wellness as an HR-only initiative. The tell: no clinical or health lead is named, and health-risk judgments are made by non-clinicians.
• Treating all employees as one population. The tell: program results are a single company-wide average that hides segment differences.
• Collecting health data without a privacy position. The tell: employees ask who sees their data and there is no written answer.

• ▸Scope statement: define the physical, mental, emotional, and safety components you will cover in one sentence each.
• ▸Target outcome: the single business outcome (retention, engagement, safety incidents) the well-being measure must connect to.
• ▸HR owner and health owner: names and decision rights for each.
• ▸Data inventory: for each component, the data source, the holder, and whether it is available now (yes/no).
• ▸Known gaps: components with no current measurement and the action to close each.
• ▸Segments: the groups (location, role, tenure, etc.) you will report separately.
• ▸Baseline by segment: current level of each measure for each segment, with the reporting date.
• ▸Link test: for each segment, whether well-being or risk measures move with the target outcome, and the evidence.
• ▸Privacy rules: who sees individual-level data, what is reported in aggregate, and how employees are informed.

1. 1Define the metric before you model anything. Split turnover into voluntary and involuntary exits, and flag high-value employees as their own group. The definition covers both kinds of exit, but retention success is mainly about keeping high-value talent, so a single blended rate hides the result you care about.
2. 2Set a baseline and a benchmark. Pull your own tenure by age group and generation. The Atlanta Fed data in the source shows median tenure falling across the board, not only among millennials, so do not attribute your turnover to one generation without checking your own numbers.
3. 3Consolidate the data. Employee data sits in finance, HR systems, and IT. Join these into one employee-level table before analysis, because speed and accuracy in answering business questions depend on it.
4. 4Build a proactive attrition model, not a rear-view report. The source says traditional approaches are failing and calls for predictive talent retention models. Use attitudinal and behavioral data (engagement survey results, engagement behaviors) as inputs, since the source treats attitudes, behaviors, and outcomes as one reinforcing loop.
5. 5Segment the model's output. Do not apply one program to every at-risk employee. Segment by value and by risk driver, then match an intervention to each segment, such as engagement, wellness, or development.
6. 6Test wellness and engagement as retention levers. Wellness benefits are factored, consciously and unconsciously, into the decision to stay or leave. Compare exit rates for participants and non-participants, and cost the program per employee.
7. 7Move retention upstream into hiring. The source defines the best candidate as a cultural fit who is engaged, a top performer, and who will stay. Use predictive models on interview and work-product data to select for tenure, and keep the interview process lean (Google caps it at four interviews).
8. 8Put the findings to work and track them. Assign each at-risk, high-value employee an owner and an action. Re-measure turnover for the segment and the cost of the interventions against the baseline.

Johnson & Johnson's wellness program shows how to cost a retention lever. Over a decade, the incidence of employees with high blood pressure fell by more than half, and leaders estimated cumulative health care savings of $250 million. From 2002 to 2008 the return was $2.71 per dollar spent. A broader study in the source puts average savings at $394 per employee per year against $159 in program cost, a net of $235 per employee, and states an ROI of $3.36 per $1. Dividing $394 by $159 gives about 2.5, not 3.36, so confirm how the ROI was calculated before quoting it. The source does not report turnover for J&J. To bring turnover into the case, split exits between program participants and non-participants within the same segment, then add avoided replacement cost to the health savings. The source says wellness benefits factor into the stay-or-leave decision, so this comparison is where a retention effect would show.

• If your blended turnover rate looks acceptable, then check the high-value segment separately. A flat overall rate can hide losses of the people you most need to keep.
• If you are tempted to blame turnover on millennials, then check median tenure by age group first. The source shows tenure declining across all age groups.
• If the engagement program is meant to raise effort and stay rates, then build it on trust, fairness, and kept two-way commitments. The source warns that mechanistic attempts to extract discretionary effort produce cynicism and disillusionment.
• If a wellness program is viewed as a perk, then present it with cost-per-employee and savings figures. The source reports it is often seen as a nice perk but not a tactical imperative, and the data suggests otherwise.
• If your attrition model flags many employees, then rank by value and act first on the high-value, high-risk segment, and match each action to the segment's driver.

• Reporting only a blended turnover rate. The tell: the overall number is stable while key people are leaving.
• Hiring a single data scientist and expecting the problem to be solved. The tell: models exist, but the data stays split across finance, HR, and IT and nothing reaches the business.
• Using a reactive exit-interview approach. The tell: you learn why people left only after they are gone, which is what the source means by traditional approaches failing.
• Pushing engagement as a way to extract effort. The tell: survey scores and cynicism both rise, and discretionary effort drops.
• Quoting a vendor or study ROI without checking its basis. The tell: the stated ROI does not reconcile with the cost and savings figures given (here $394 vs $159 against $3.36).

• ▸Turnover definition: state how voluntary and involuntary exits are counted and the period covered.
• ▸Baseline rates: record voluntary, involuntary, and high-value-employee turnover separately.
• ▸Tenure by age group/generation: enter your median tenure for each group to compare with the external trend.
• ▸Data sources joined: list the finance, HR, and IT tables merged and the employee key used.
• ▸Model inputs: list the attitudinal and behavioral variables (engagement survey, engagement behaviors, wellness participation).
• ▸Segments: define each segment by value and risk driver.
• ▸Intervention per segment: name the action, its owner, and its cost per employee.
• ▸Comparison group: describe how participants and non-participants are compared.
• ▸Result and review date: record post-intervention turnover for the segment against baseline and the next review date.

1. 1Write the ELV equation from the definition before touching data: ELV = value contributed over the whole tenure (productivity and performance, multiplied by length of service) minus acquisition cost minus development cost. The source passages name the inputs but not a formula, so fix your own definitions in writing. Decide what counts as 'value' and what counts as 'cost' for each role.
2. 2Segment the workforce before you model. The source's own warning is that analytics is 'not a one-size-fits-all', and its retailer case shows that some roles (mid-level store managers) are linchpins for the whole value chain. Pick one or two critical segments first. Do not build a company-wide average.
3. 3Map each segment's tenure onto the lifecycle stages the source uses: Acquire (including the honeymoon period when expectations meet reality), Advance (growing in experience, responsibility, money), and Retain (the 'hold' part of hire and hold). Assign every cost and value data point to one stage so you can see where value is created and where it leaks.
4. 4Build the cost side. Collect acquisition cost per hire (sourcing, hiring) and development cost per employee. Then compare your acquisition cost with your competitors' if you can. The source asks directly whether your talent acquisition costs are higher than your competitors'.
5. 5Build the value side with performance and productivity measures. Use the outputs of your performance management and onboarding work, for example time to productivity as an early-tenure measure. Use measures tied to company objectives, not to activity.
6. 6Model length of service with a retention or churn view. Isson carried customer lifetime value segmentation and churn models from telecom to talent. Estimate the likely remaining tenure by segment and identify which segments are more likely to defect to competitors.
7. 7Compute ELV per segment and per lifecycle stage. Then find the levers: shorten time to productivity, advance people so they stay, cut acquisition cost, or retain the highest-ELV segments. Rank levers by effect on ELV, not by ease.
8. 8Close the loop. Re-run ELV after each intervention. Track the three components (productivity, tenure, net cost) separately so you can tell which one moved. The Deloitte finding shows why this matters: 75 percent call talent analytics important and only 8 percent say they are strong at it.

The source describes a large retailer that revolutionized its supply-chain management and then found that mid-level store managers were the linchpins deciding whether the efficiently delivered merchandise was actually bought. Treat store managers as your first ELV segment. Acquire stage: record what it costs to source and hire a store manager, and how long the honeymoon-period expectations gap lasts before they are productive. Advance stage: record what you spend to grow them in experience, responsibility, or pay. Value: link each manager's performance to the store outcome that the retailer says depends on them. Retain: estimate how long managers stay and whether this segment is more likely to defect to competitors. The ELV insight is that a manager who stays and advances compounds value, while one who leaves early forfeits the acquisition and development spend. The source gives no figures for this case, so the numbers must come from your own data. The structure tells you where to look first: the segment that most affects revenue, and the stage where value leaks.

• If a segment's performance drives outcomes for others (the store-manager linchpin pattern), then model it first and in the most detail.
• If acquisition cost exceeds a competitor's or the segment's expected tenure is short, then fix hiring cost or retention before spending more on development.
• If employees are in the honeymoon period and expectations are being tested against reality, then treat early-tenure measures like time to productivity as leading indicators of ELV.
• If high performers have no path to grow in experience, responsibility, or money, then expect lower tenure and add advancement actions, since the source ties advancement directly to retention.
• If you cannot define value for a role, then do not publish an ELV number for it. Segment it out and use cost and tenure measures only.

• Treating employees as costs only. The source chapter explicitly asks 'Are employees costs or assets?' The tell: your model has a cost column and no value column.
• Using one company-wide average ELV. The source says the approach is not one-size-fits-all. The tell: the same ELV for a linchpin role and a low-impact role.
• Counting tenure as value in itself. ELV depends on productivity and performance during that tenure. The tell: retention is celebrated for people whose performance is low.
• Ignoring early-tenure and development costs. The tell: ELV is calculated from salary and output alone, with nothing for acquisition or ramp-up.
• Starting analytics late. Harris notes that HR is the area companies look at last. The tell: ELV is requested only after turnover has already spiked, with no baseline data.

• ▸Segment / role: name the group (e.g., mid-level store managers) and why it is critical to outcomes.
• ▸Acquisition cost per hire: total sourcing and hiring cost for this segment, with a competitor comparison if available.
• ▸Development cost per employee: training, onboarding, and advancement spend by lifecycle stage (Acquire / Advance).
• ▸Time to productivity: how long until a new hire is performing at expectation (honeymoon-period check).
• ▸Value contributed per period: productivity or performance measure linked to a company objective, and the source of the data.
• ▸Expected tenure: modeled length of service and the defection risk (competitor pull) for this segment.
• ▸ELV estimate: (value per period × expected tenure) − acquisition cost − development cost.
• ▸Biggest leak: the lifecycle stage where value is lost (slow ramp, no advancement, early exit).
• ▸Intervention and re-test date: the one lever you will change and when you will recompute ELV.

1. 1Start from the organization's purpose and strategic goals, not from HR tooling. The foreword's point is that recruitment begins with purpose, so write down which workforce outcomes the business needs before you pick any ROI metric.
2. 2Map spend and outcomes across the seven pillars named in the passages: workforce planning, sourcing, acquisition, engagement, promotion and performance, retention, and wellness, health, and safety. For each pillar, list what you spend and what result you expect, so ROI is tracked across the full talent cycle.
3. 3Build a source-of-hire attribution view. Assess the performance of every source (career site, job boards, social media, employee referrals, university campuses) on two measures: effort spent to acquire talent and how the hires perform once hired.
4. 4Use that attribution to decide which source suits which position, then adjust tactics and hiring budget accordingly. Move budget away from sources that cost effort but produce weaker performers.
5. 5Add location and market intelligence before committing to a hiring plan. Ask how popular the skill set is, how popular it is within the function and at the experience level, how many companies want similar roles, and which nearby schools teach relevant courses. Use the answers to judge supply and demand.
6. 6Turn the market data into workforce-planning recommendations (how many people can we hire? what skills?). Use them to reset hiring expectations with decision makers and to say when and where to hire.
7. 7Apply the three analytics levels in order: descriptive (what has happened), predictive (what must be done to hit future goals), prescriptive (how to do it). Report ROI changes at each level.
8. 8Add social and mobile channels to sourcing as measured experiments, tracking quality of hire, time to hire, reach of quality candidates and cost. Make sure candidates can search, apply and accept an offer from a phone. Keep ROI tied to the organization's mission and review it on an ongoing basis.

The passages describe a company, Bloomberg, that applied this logic to a competitive talent market. Its People Analytics team built an attribution model that scores every source of hire (the Bloomberg career site, traditional job boards, social media, employee referrals, university campuses). Talent management can now anticipate which source to use for a specific position and adjust tactics and hiring budget to match. The team also used location and intelligence models to see when and where to source. They asked how popular a skill set is, how many companies are hiring for similar roles, and which nearby schools teach relevant courses. In markets where "there is a dogfight" for skilled talent, this changed the conversation between managers and People Analytics. The team could now offer data-driven workforce-planning recommendations, such as how many people the company can hire, and could manage hiring expectations accordingly. The passages report no dollar figures, so the ROI gain shows up as better-targeted budget and more realistic hiring plans, not as a stated percentage.

• If a source of hire consumes high acquisition effort and its hires perform below those from other sources, then shift budget to the stronger source for that position.
• If location intelligence shows many companies chasing the same skill set in a market, then reset the hiring-volume and timeline expectations with managers, or look at nearby schools or other locations, before opening requisitions.
• If a role needs passive candidates not reachable through resume databases and job boards, then add social sourcing, and judge it on quality of hire, time to hire and cost.
• If candidates cannot complete search, application and offer acceptance on a phone, then fix the mobile experience first, because mobile is the top communication platform and the second most important channel after referrals.
• If a proposed analytics effort cannot be linked to the organization's purpose and goals, then drop it, since talent analytics needs ongoing alignment with mission.

• Averaging ROI across all hiring: the budget stays spread evenly over every source. The tell is that no one can say which source produces the best performers.
• Measuring acquisition cost but not post-hire performance: sources look efficient until the hires underperform. The tell is that the attribution report ends at the offer-accept date.
• Hiring plans that ignore market supply and demand: the targets are unrealistic and roles stay open. The tell is that managers ask for headcount in a market where many companies compete for the same skill set, and no one has checked.
• Letting intuition stand in for evidence: the passages note that best practices based on anecdote or instinct no longer satisfy line management. The tell is that budget requests rest on "this is how we've always hired."
• Treating ROI as a recruiting-only metric: the other pillars (engagement, retention, promotion, wellness) are left out of the picture. The tell is that the dashboard stops at acquisition.

• ▸Role / position: name the job family being filled.
• ▸Business goal served: one line linking the role to organizational purpose or strategic goal.
• ▸Sources used: list each (career site, job boards, social media, referrals, campus, other).
• ▸Acquisition effort per source: record the effort and spend to acquire hires from each source.
• ▸Post-hire performance per source: record how hires from each source perform once on the job.
• ▸Market supply and demand: note how popular the skill set is, how many companies want similar roles, and which nearby schools teach relevant courses.
• ▸Hiring expectation set with decision makers: state how many people can be hired, which skills, when and where.
• ▸Budget and tactic adjustment: state the shift made and the source it moves toward or away from.
• ▸Review date and result: set the date you will compare the new results with this baseline.

1. 1Start with the business, not the data. Write down how your company makes money, what makes it competitively formidable, and where people create that advantage. Berry says these three things matter more than statistical credentials.
2. 2Turn a business challenge into a question. Use the passages' templates: "How does talent affect execution of our business's strategy?" or "What capabilities are necessary to help our business succeed?" Pick one question per initiative.
3. 3Name the people 'levers' behind the outcome. Berry's pattern is to learn what makes the business successful, define the people levers of that success, then decide how to influence them. Record each lever next to the business result it moves (revenue, cost, productivity, profitability).
4. 4Get senior leadership buy-in and tie People Analytics goals, strategy and activities to the organization's business goals. These are the first two items on the 'baker's dozen' best-practice list. Explain the value in language business leaders understand, because misunderstanding most typically ends in defunding and dismantling.
5. 5Start small and master the human capital data you already hold. Take one focused use case, such as quantifying total workforce cost or evaluating a facility closure versus the investment needed to make it profitable. Use experts in traditional analytics to map the approach, since HR analytics is new.
6. 6Check any talent-management tool or provider against the passage criteria before buying: data security and privacy, integration with HR and IT systems, customizability, employee life cycle reporting, cloud-based SaaS, mobile/social with Big Data capacity, social collaboration, intuitive interface, scalability.
7. 7Quantify the impact of each HR program on business outcomes, and optimize the company's investment in people. This is the step that connects the work to improved business performance.
8. 8Expand only after the first use case shows results, following the 'dream big, start small' and baby-steps approach. Choose initiatives by what is good for your business, not by what you heard about at a conference.

Berry, a HR analytics leader with a 20-plus-year career, describes using fact-based decision making against business issues across 'target rich' environments. One case: evaluating the cost of closing a nonprofitable facility versus the investment required to make it productive and profitable. The business question is financial, the people lever is the workforce at that facility, and the data answers a decision that directly moves profitability. A second case: quantifying the total cost of a workforce and optimizing it through improved planning, analysis and forecasting. A third: defining what current and prospective employees most value in total rewards, how the company measures up, and what to change to improve attraction, engagement, development and retention. In each, the common components are a focus on people, solving a business issue, and using data to drive improved outcomes. The passages give no numeric results, so judge success by the business metric you named at the start.

• If you can't state the business question in one sentence tied to how the company makes money, then don't start the analysis; formulate the question first.
• If stakeholders don't understand what the analytics will do, then explain and demonstrate its value in business language before building more, because misunderstanding leads to defunding.
• If a candidate initiative comes from a conference or book rather than a business need, then drop it unless you can name the business lever it moves.
• If your data and processes aren't mastered yet, then keep the scope small and use traditional-analytics experts instead of buying broad technology.
• If a vendor fails on data security/privacy, integration, or scalability, then rule it out regardless of features.

• Chasing the 'bright, shiny object': the initiative isn't linked to a business goal. Tell: no one can name the business metric it should move.
• Treating analytics acumen as the main skill and neglecting business acumen. Tell: polished models that answer questions leadership never asked.
• Being misunderstood by senior stakeholders. Tell: HR is not seen as a source of data and insight, and funding is questioned or cut.
• Going big before mastering existing human capital data. Tell: new tools and dashboards while basic data stays unused or fragmented.
• Buying technology without checking integration, security or scalability. Tell: the tool can't connect to HR/IT systems or can't handle growing data volume.

• ▸Business question: write the question in one sentence (e.g., how does talent affect execution of our strategy?).
• ▸Business goal supported: name the organizational goal this ties to (revenue, profitability, cost, market position, operational effectiveness).
• ▸People lever: identify the workforce factor that influences that goal.
• ▸Data already available: list the human capital data you can use now, without new tools.
• ▸Executive sponsor and buy-in: name the senior leader and how you will explain the value in business language.
• ▸Pilot scope (start small): define the single use case and its boundary.
• ▸Success metric and baseline: state the business metric and its current value.
• ▸Tool/provider check: record pass/fail on security, integration, customizability, life cycle reporting, scalability.
• ▸Next step if it works (dream big): describe the next expansion once the pilot shows impact.

1. 1Write the planning target in the definition's four terms: how many people, with which skills, at which location, by which date. Tie each target to a named business goal. A headcount number with no goal attached is not a plan.
2. 2Split the work into two forecasts, demand and supply. Demand is the workforce the business goals require. Supply is the workforce you will have after attrition, movement, and hiring. Keep them as separate models, because the plan is the gap between them.
3. 3Build the supply side from the other pillars. Retention and attrition predictive models (the book segments retention insights), sourcing and acquisition pipeline data, and onboarding outcomes all feed future supply. Treat workforce planning as the pillar that consumes the others' outputs.
4. 4Inventory your data and tools before modeling. The book names a 'Data and Tools Challenge' as a barrier to every pillar. List which HR, finance, and business-unit sources you can actually pull, and mark each gap as fixable now or later.
5. 5Run the work through the book's IMPACT cycle, the structure of the 'Making an IMPACT' section of Chapter 4. Chapter 5 reuses it for sourcing, so it is a repeatable loop and not a one-off project. The passages you have do not spell out its stages. Read Chapter 4 for them and attach each of your steps to a stage.
6. 6Convert each gap into a decision an owner can act on: hire, develop, retain, or source differently. Name the owner and the date. A forecast that ends as a report changes nothing.
7. 7Present the gap to line management in business terms. The endorsements stress that anecdote and instinct no longer satisfy line management, so show the evidence behind each projection.
8. 8Re-run the forecasts on a fixed cadence and compare projected with actual supply and demand. Refine the models where they missed.

The passages name a real case without giving its detail: Christophe Paris, Josée Gauvreau, and Cedric Lepine of Société de Transport de Montréal (STM) shared a workforce planning analytics success story for Chapter 4. Mark Berry (formerly ConAgra Foods, Workforce Analytics Planning) and James Gallman (General Electric, Strategic Workforce Planning) are listed as practitioners in the same area. No figures from these cases appear in the supplied text, so read the chapter for the specifics. The structure below is the transferable part. Start with a business goal, such as keeping a service running. State the demand as roles × skills × locations × dates. Project supply by taking current staff and subtracting predicted attrition from a retention model segmented by role. Add expected hires from your sourcing and acquisition pipeline. Whatever remains is the gap per role and location. Assign each gap an action and an owner, then check actual attrition against the prediction at the next cycle.

• If you cannot name the business goal a headcount target serves, then stop and define the goal first. Analytics on an unanchored target produces precision without relevance.
• If your attrition is concentrated in particular segments, then forecast supply by segment, not with one company-wide rate. The book's retention work stresses segmentation.
• If the demand and supply forecasts rest on different data definitions, then reconcile the definitions before comparing them, since the gap is meaningless otherwise.
• If a gap's cause is sourcing or acquisition capacity, then route the action to those pillars. If the cause is attrition, route it to retention. Do not default every gap to 'hire more'.
• If line managers still rely on gut feel, then show them the forecast against their own instinct at the next cycle. Accuracy builds adoption.

• Planning on headcount only: the plan lists totals and ignores skills, place, and timing. The tell is a plan that balances in aggregate while specific roles or sites stay unfilled.
• Treating workforce planning as a standalone HR report. The tell is that no one in sourcing, retention, or the business units uses its outputs.
• Starting modeling before checking data and tools. The tell is months of work stalled on missing or inconsistent sources.
• Forecasting once and never validating. The tell is that nobody can say how last year's projection compared with actual outcomes.
• Falling back on instinct under pressure. The tell is that hiring decisions are justified by anecdote even though a model exists.

• ▸Business goal served: name the goal this role supports.
• ▸Role and skill set: the specific skills required, not just a title.
• ▸Location: where the people are needed.
• ▸Needed-by date: when the demand must be met.
• ▸Demand forecast: the number required and the basis for the estimate.
• ▸Current staff: the number in the role today.
• ▸Predicted attrition: the expected losses, with the segment and model used.
• ▸Expected inflow: hires and internal moves from the sourcing and acquisition pipeline.
• ▸Projected gap: demand minus (current − attrition + inflow).
• ▸Action, owner, and review date: hire, develop, retain, or source differently, who owns it, and when you compare projected with actual.

1. 1Start from the problem the foreword names: job boards, college recruiting trips, open houses and advertising are used by everyone, so they produce similar results and no advantage. Write down which of these channels you rely on today. Treat each one as a baseline to beat, not as a differentiator.
2. 2Reframe the problem as the fishing metaphor does. The talent ocean is not overfished for your company's needs; the issue is that many boats are fishing. Your job is to find where your target candidates are that other boats are not working, so define the target profile first and fish for exactly that.
3. 3Inventory every sourcing data channel you can touch: job boards, social media, employee referrals and any other source feeding candidates to you. For each channel, log what candidate data it produces and whether it reaches active candidates, passive candidates, or both. The definition of this practice covers both.
4. 4Replace gut-feel judgments about sources with measured ones. The endorsements criticize talent decisions that rest on 'gut feelings and immeasurable interpersonal relationships.' Pick a small set of measures per channel (volume, how well candidates fit the profile, what happens to them after sourcing) and record them consistently.
5. 5Connect sourcing data to what happens downstream. Sourcing is one pillar of seven, sitting between workforce planning and acquisition/hiring. Link each sourced candidate to later outcomes so a channel is judged by the people it produces, not by raw lead counts.
6. 6Add predictive analytics to the assessment step. The source's position is that a world-class talent acquisition system needs prediction to find the people you are fishing for. Use the linked channel-to-outcome history to rank channels and candidate profiles by likelihood of fit before you spend recruiter time.
7. 7Account for mobile in how candidates are found and engaged. The chapter treats mobile impact as its own topic, so check that your engagement touchpoints work on the devices candidates use.
8. 8Run the work as a repeating cycle, not a one-off project. The chapter closes with 'Putting the IMPACT Cycle into Action,' so schedule a recurring review in which findings change channel spend and outreach, then re-measure.

The source supplies an argument rather than a numeric case, so this example follows the foreword's logic. A company sources through the standard mix: job boards, college trips, open houses and advertising. The foreword says everyone uses these and the results are 'similar rather than unique,' so the company has no edge. It tags each channel, reviews what candidates from each channel went on to do, and finds that some channels return the target profile and others return volume with no fit. Using the fishing metaphor, it stops competing in crowded waters and moves effort toward channels and passive candidates that other 'boats' ignore, using predictive scoring to decide where to cast. It then reviews the results on a cycle and shifts budget again. The source gives no figures for this, so measure your own.

• If a channel is one everyone uses (job boards, college trips, open houses, advertising) and you cannot show it yields better candidates than competitors get, then treat it as a baseline cost and look for sources that differentiate you.
• If a sourcing decision rests only on recruiter intuition or relationships, then require a measured channel result before it changes budget.
• If a channel produces candidates but you cannot trace them to downstream outcomes, then fix the data link first, because you cannot judge that channel yet.
• If the channel data covers only active applicants, then add a way to identify and reach passive candidates, since the practice covers both.
• If a review shows a channel's performance has shifted, then reallocate effort and re-measure on the next cycle instead of waiting for an annual review.

• Defending traditional channels because 'we have always used them.' The tell: you cannot name a result from them that competitors lack.
• Collecting data without a target profile. The tell: dashboards of volume with no view on candidate fit.
• Measuring only the sourcing step in isolation. The tell: a channel looks strong at the top while later outcomes are never checked.
• Judging sourcing by gut feel and relationships. The tell: source decisions are explained by anecdotes, not numbers.
• Treating analytics as a one-time project. The tell: the analysis was done once and no cycle exists to act on it and re-measure.

• ▸Channel: name the source (job board, social media, referrals, other).
• ▸Candidate type reached: record active, passive, or both.
• ▸Is it a commodity channel?: note whether competitors routinely use it, yes or no.
• ▸Target profile defined: write the profile you are fishing for in this channel.
• ▸Data captured: list what candidate data the channel gives you.
• ▸Downstream outcome link: state how you trace a sourced candidate to later results.
• ▸Fit result: record how well this channel's candidates match the profile, with the measure used.
• ▸Mobile readiness: note whether engagement works on mobile devices.
• ▸Decision for next cycle: write keep, shift, or cut, with a review date.

1. 1Anchor the practice in purpose before touching data. The foreword is explicit that recruitment does not start in the employment section of HR. It starts with organizational purpose, goals and strategic plans, and a target like 'increase market share by x percent' is a wish, not a purpose. Write one sentence on the organization's role and the roles that purpose requires, then take the headcount from workforce planning, the pillar upstream of acquisition.
2. 2Define the outcome the model predicts. The construct targets candidates who are successful, productive and long-tenured, so write one measurable definition for each, per role family. Success and productivity come from the performance assessment pillar; tenure comes from the retention and attrition data. If you cannot name the field in your systems that holds each outcome, fix that before building anything.
3. 3Map the selection funnel using the source's four stages: application, preinterview assessment, interviews, and predictive selection. For each stage, list the data captured (application fields, assessment results, interviewer ratings), who owns it, and whether it can be joined to a hired person's later performance and tenure record. Stages that cannot be joined cannot be validated.
4. 4Build the descriptive layer first: what has happened up to the present. For past hires and past applicants, report how many moved through each stage and how the hires performed and stayed. This baseline exposes where today's process, which the foreword says is common to every competitor, produces similar rather than distinctive results.
5. 5Move to the predictive layer. Relate stage-level signals (application, preinterview assessment, interview) to the three outcomes for people already hired, then combine them into one selection score. The chapter title 'Putting It All Together' signals that the model's value comes from combining stages, not from any single screen. The passages do not specify an algorithm, so choose one your team can explain and validate.
6. 6Add the prescriptive layer: turn the score into instructions. The foreword's doctor analogy puts the prescription on the paper taken to the pharmacy, stating how the treatment is administered. Specify which score range moves forward, which assessment is required before interview, and what the interviewer must probe. A score nobody acts on is only description.
7. 7Test the interview against the model. The chapter frames interviews as 'separating the wheat from the chaff', and the endorsements criticize talent decisions that rest on gut feelings. Record interviewer recommendations separately from the model score. Compare each against later performance and tenure, and weight the interview according to what it shows.
8. 8Close the loop and govern it. Feed onboarding, performance and attrition outcomes back into the model so it keeps learning. Plan for the employee data privacy backlash that the book's final chapter lists as a coming trend: document which candidate data you use and why.

The source passages give the structure of this practice, not a numeric case, so this example applies the foreword's own doctor analogy to hiring. Descriptive: your sales-role funnel report shows that most hires came through interviews and that a third left early. That is the patient saying 'I have sinus congestion': it states what happened. Predictive: you relate each hire's application data, preinterview assessment and interview rating to later performance and tenure, and find which signals separate the long-tenured, productive hires from the early leavers. That is the doctor applying knowledge to determine the treatment. Prescriptive: you write the rule that goes on the paper to the pharmacy: candidates must complete the assessment before interview, interviewers see the score range, and only candidates in the qualifying range advance. You then track the next cohort's performance and tenure against the old baseline. All figures in this scenario are illustrative placeholders; none come from the passages.

• If the role's purpose and headcount are not defined by workforce planning, then stop and fix that first, because a model optimizing against a wish ('hit x percent') selects for nothing in particular.
• If a funnel stage's data cannot be linked to the hired person's later performance and tenure, then do not score on it yet; collect and join it first.
• If interviewer judgment does not predict later performance or tenure better than the assessment and application signals, then reduce its weight and restructure the interview, rather than defending it as intuition.
• If your process is the same job boards, campus trips and advertising everyone else uses, then expect similar results; move to predictive selection to create a difference.
• If the model's output is a score with no stated action, then it is still descriptive; add the prescriptive rule (who advances, what is assessed, what is probed).

• Treating analytics as a recruiting tool rather than a strategy tool. Tell: the model has no link to organizational purpose or the workforce plan, and success is defined as time-to-fill.
• Predicting only the hire, not the outcome. Tell: no field exists for post-hire performance, productivity or tenure, so you cannot say whether the model picks long-tenured employees.
• Letting gut feeling override the model without measurement. Tell: interviewers overrule scores and nobody compares those overrides with later results.
• Stopping at description. Tell: dashboards show funnel volumes and sources but no one can state the rule that changes who gets selected.
• Ignoring candidate data privacy. Tell: you cannot say which candidate data feeds the model or why it is permitted, which leaves you exposed to the privacy backlash the source anticipates.

• ▸Organizational purpose and role need: State the purpose in terms of role in society and the strategic goal this role serves, not a percentage target.
• ▸Success measure: Name the performance metric and the system field that holds it.
• ▸Productivity measure: Name the output metric and its source.
• ▸Tenure measure: Define 'long-tenured' for this role and the attrition data source.
• ▸Funnel data inventory: List data captured at application, preinterview assessment and interview, with owner and join key to post-hire records.
• ▸Descriptive baseline: Record stage-to-stage movement, hire outcomes and early attrition from past cohorts.
• ▸Predictive signals and method: List the signals tested, the chosen method, and how you validate it against the three outcomes.
• ▸Prescriptive rule: State the score range that advances, the required assessments, and the interview focus.
• ▸Interview vs. model check: Specify how interviewer ratings are logged and compared with later outcomes, and review cadence.
• ▸Privacy and feedback loop: Document the candidate data used, the justification, and how onboarding and attrition data return to the model.

1. 1Start with the culture definition, not the dashboard. Chapter 7 opens with Organizational Culture before it reaches the onboarding process. Write down the values and business vision a new hire is supposed to absorb, in terms specific enough to observe. Only 12 percent of HR and business leaders in the Deloitte survey say they have a program to define and build a strong culture, so assume yours is undefined until you have it on paper.
2. 2Map your onboarding process as discrete stages. The chapter separates the Onboarding Process from the Stages of Onboarding. List every stage a hire passes through, from offer acceptance to full productivity. Give each stage an owner, a start event, and an end event, so you can later measure what happens between them.
3. 3Inventory early employee needs per stage. The chapter treats Early Employee Needs as its own topic. For each stage, record what a new hire needs to function and feel integrated, then attach a data source to each need: HRIS records, a pulse question, a manager check-in. A need with no data source stays invisible.
4. 4Run your design through the OPEN Analytical Framework. The chapter names it as the structure for effective onboarding. Use it as the checklist that ties culture, stages, and needs to measures. The passages do not expand the acronym, so work from the book's own chapter text rather than guessing the letters.
5. 5Define outcome measures before launching anything. The chapter pairs Time to Productivity with 'other outcome measures.' Fix a definition of 'productive' per role, and add engagement and early retention as companion outcomes. This gives you a baseline and a finish line for every cohort.
6. 6Connect to the neighboring pillars' data. Onboarding sits in one pillar with Culture Fit and Engagement. Link acquisition and hiring data (what you knew at selection) to onboarding outcomes, and onboarding data to later engagement and attrition. The predictive model needs that continuous record across the hire's lifecycle.
7. 7Build the onboarding predictive model last. The chapter places Create an Onboarding Predictive Model after the outcome measures. Use early-stage signals and hire attributes to predict time to productivity and early departure. Use the predictions to intervene with at-risk hires during the early stages, then compare cohorts.
8. 8Close the loop ('Tying It All Together'). Review each cohort against baseline, change the stage or need that underperformed, and re-measure. The goal is a repeating cycle, not a one-time program.

The source passages give no company onboarding case, so this example uses their one concrete benchmark: the Deloitte global survey of more than 3,300 HR and business leaders in 106 countries. It found that 50 percent called engagement 'very important' (up from 26 percent the prior year), but 60 percent said they lack an adequate program to measure and improve it. Only 12 percent had a program to define and build a strong culture, and only 7 percent rated themselves excellent at measuring, driving, and improving engagement and retention. To apply this, audit your own onboarding against those three gaps. First, can you state your culture in writing (the 12 percent test)? Second, do you measure engagement for new hires at defined stages (the 60 percent test)? Third, can you tie the result to retention (the 7 percent test)? Any 'no' tells you where to start: define culture first, then stage-level measurement, then the predictive link to retention. The book's stakes statement explains why this matters: training time, experience, and relationships built with a hire are all lost the moment that talent leaves.

• If you cannot state your culture in observable terms, then define it before measuring culture fit, because there is nothing to measure against.
• If a stage has no owner, start event, or end event, then fix the process map before adding analytics to that stage.
• If time to productivity has no per-role definition, then agree on one with managers before building any predictive model.
• If you have outcome baselines for at least one hire cohort, then build the predictive model. If you do not, then collect baselines first.
• If a model flags an early-attrition risk, then act through the early-needs checklist for that stage, and re-measure the cohort to see whether the action worked.

• Measuring satisfaction without defining culture. The tell: survey scores exist but nobody can say which values they reflect.
• Treating onboarding as a single event. The tell: data is collected only at day one or at the end, with nothing between stages.
• Reporting time to productivity with no agreed definition of productive. The tell: managers dispute the numbers and the metric differs by team.
• Jumping to a predictive model before baselines and outcome measures exist. The tell: the model has no validated target and its predictions cannot be checked.
• Keeping onboarding data siloed from hiring, engagement, and retention data. The tell: you cannot say whether selection signals predicted onboarding outcomes or whether onboarding outcomes predicted later attrition.

• ▸Culture statement: list the values and business vision a new hire should absorb, in observable terms.
• ▸Role / cohort: name the role and hire cohort this plan covers.
• ▸Onboarding stages: list each stage with its owner, start event, and end event.
• ▸Early employee needs per stage: state what a hire needs at each stage and the data source that shows whether it is met.
• ▸Productivity definition: define 'productive' for this role and the target time to reach it.
• ▸Outcome measures: list time to productivity plus engagement and early-retention measures, with baseline values.
• ▸Linked data sources: name the hiring, engagement, and attrition datasets joined to this plan.
• ▸Predictive model target: state what the model predicts (e.g., early departure or slow productivity) and the early signals it uses.
• ▸Intervention and review: define the action taken on a flagged hire and the date you re-measure the cohort.

1. 1Start from a business question, not a survey. The source frames engagement around questions such as 'How engaged are our top performers in our key business strategies?' and 'Do we know which talents are at risk of leaving? What are we doing to keep them?' Write one or two such questions, and name the decision each answer will change, before you collect anything.
2. 2Establish the survey baseline. The Chapter 8 sequence starts with why engagement matters, then engagement surveys. Run the survey so every response can be joined to employee attributes you already hold (role, team, tenure, performance group). Without that join, you get only company-wide averages.
3. 3Make the survey predictive. Chapter 8 treats this as a separate step from running the survey. Pick an outcome that matters to the business (attrition, performance, productivity) and test which survey items and segments are associated with it. Rank items by their link to the outcome, not by how low the scores are.
4. 4Add behavioral data to the survey. The source says to move beyond the survey to engagement measures, and the Preface lists real-time signals: engagement, actions, sentiment, productivity, location, quality and aspirations. The task definition adds communication patterns and participation. Pick two or three behavioral signals you can actually obtain. Compare them to survey results for the same groups to see where self-report and observed behavior agree and where they diverge.
5. 5Segment before you interpret. The source warns against decisions based on averages (Chapter 14: 'People Analytics Moves beyond the Averages'). Cut results by the groups that drive value, such as top performers and critical roles. Identify drivers per segment rather than for the whole company.
6. 6Tie engagement to the adjacent pillars. Engagement sits in the pillar 'Onboarding, Culture Fit, and Engagement' and feeds retention and performance. Feed the engagement findings into your attrition model, and use onboarding outcomes as an early engagement read.
7. 7Design the intervention and name its owner. Link each driver you find to one action, an accountable manager, and a re-measure date. The practice exists to 'inform interventions', so a finding with no owner is not finished.
8. 8Set privacy rules before using behavioral data. The source lists 'Employee Data Privacy Backlash' among the future trends. Decide what you collect, who sees it, and at what level of aggregation, and tell employees. Do this before you launch, not after.

The source passages do not include an engagement case with figures, so this is an illustrative walk-through built only from the chapter structure, with no invented numbers. Start with the question 'How engaged are our top performers in our key business strategies?' Run the survey with responses joined to performance group and role. Next, test which survey items relate to departures in the segment you care about, which is the 'make it predictive' step. Then pull one or two behavioral signals, such as participation, for the same segment, and check them against the survey. Suppose a team's survey scores look fine but its participation is falling. That mismatch is the cue to investigate before the survey average misleads you. Assign the finding to the team's manager with an intervention and a re-measure date, and feed the segment's engagement score into your retention model as an input.

• If a survey item cannot be linked to an outcome (attrition, performance, productivity), then do not build an intervention on it. Treat it as descriptive until it is tested.
• If survey scores and behavioral signals disagree for a group, then investigate that group before acting on either source.
• If an average hides a high-value segment (top performers, critical roles), then report and act on the segment, not the average.
• If a finding has no owner and no re-measure date, then it is not ready to leave the analytics team.
• If a behavioral data source has no clear privacy rule or employee disclosure, then do not use it yet.

• Treating the survey as the whole practice. The tell: reports contain only scores and trends, with no outcome linkage and no behavioral measures, even though the source places 'moving beyond the survey' as its own step.
• Reporting only company-wide averages. The tell: every team gets the same generic action plan, and top-performer or critical-role engagement is never shown separately.
• Collecting data without a decision attached. The tell: you cannot name the action any given metric would change.
• Ignoring privacy. The tell: employees learn about behavioral monitoring after it starts, which is the backlash the source anticipates.
• Running engagement apart from retention and onboarding. The tell: the engagement team and the attrition model use different employee data and never share findings.

• ▸Business question: the decision-linked question this analysis answers (e.g., engagement of top performers in key strategies).
• ▸Target segment: the group analyzed (role, team, performance group), not the company average.
• ▸Outcome measure: the business outcome you test against (attrition, performance, productivity).
• ▸Survey items tested: the items you checked against the outcome, and the result for each.
• ▸Behavioral signals used: the signals (e.g., participation, communication patterns) and the data source for each.
• ▸Survey vs. behavior check: where the two agree or diverge, and what you will investigate.
• ▸Intervention: the single action tied to the strongest driver.
• ▸Owner and re-measure date: the accountable manager and when you re-run the measure.
• ▸Privacy basis: what is collected, aggregation level, and how employees were informed.